A self structuring artificial intelligence framework for deep emotions modeling and analysis on the social web
| Autor: | Daswin De Silva, Achini Adikari, Sze-Meng Jojo Wong, Nishan Mills, Gihan Gamage, Damminda Alahakoon |
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| Rok vydání: | 2021 |
| Předmět: |
Word embedding
Relation (database) Computer Networks and Communications Computer science business.industry Emotion classification Sentiment analysis Novelty 020206 networking & telecommunications 02 engineering and technology Social web Personality psychology Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Emotional expression Artificial intelligence business Software Uncategorized |
| Zdroj: | Future Generation Computer Systems. 116:302-315 |
| ISSN: | 0167-739X |
| DOI: | 10.1016/j.future.2020.10.028 |
| Popis: | © 2020 Elsevier B.V. The social web has enabled individuals from all walks of life to openly express their emotions and sentiment in relation to current affairs, local issues and personal circumstances. Within the social web, social media encompasses deep emotional expressions that reflect a multitude of personalities and behaviors. Existing research in this space is heavily focused on supervised sentiment analysis and emotion detection, with limited work on modeling these deep emotions, mixed emotions and variations of emotional behaviors from unlabeled and unstructured social media conversations. In this study, we propose a comprehensive framework based on the principles of self-structuring artificial intelligence for emotion modeling and analysis that systematically integrates the modeling capabilities at a granular level on unstructured, unlabeled social media data. The research contributions of this framework are the detection, analysis and synthesis of deep emotion intensity, emotion transitions, emotion latent representations, and profile-based emotion classification. The self-structuring artificial intelligence framework amalgamates an ensemble of novel algorithms to eventuate these contributions. These algorithms extend the current state-of-the-art of natural language processing techniques, word embedding, Markov chains and growing self-organizing maps, specifically for deep emotions modeling and analysis. The framework is empirically evaluated on anonymized conversations from online mental health support forums. The outcomes identify profile-based emotion characteristics, emotion intensities, transitions and an overall latent representation across three distinct mental health groups in these forums. These outcomes are comprehensive in comparison to existing work which singularly focuses on sentiment analysis or emotion detection. The validity and effectiveness of its application on a real-world social media setting further establish the methodological novelty of this ensemble of self-structuring artificial intelligence for deep emotions. |
| Databáze: | OpenAIRE |
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